There are numerous systems and methods that extract information from digital files. These are called information retrieval routines. Most of these provide a single piece of information, and it is necessary to aggregate and process this information to automatically generate metadata that is often contextual or subjective in nature. There are many approaches to doing this, including semantic analysis, searching for key words or terms, and searching for standardized associated licenses. An effective method must apply multiple such approaches.
There is a need to generate metadata in a way that enables the metadata to be used by other applications such as authoring tools, learning content management systems and rights management systems. It is also required to support continual improvement of the results by making it possible to substitute new or improved information retrieval routines for existing ones and by making it possible to combine the output of these routines in new ways.
Another challenge of metadata generation is the fact that digital objects often are comprised of multiple smaller objects. For example, a Web page might contain text, images, a movie and an interactive quiz written in a format such as Adobe Flash. It is advantageous to generate, retain and aggregate information on smaller objects that comprise a larger object. This leads to a significant performance advantage over prior art.
Current systems cannot estimate complex quantities such as typical learning time, effectively search for rights licenses or be used to determine whether a learning object or knowledge object is in line with a particular design paradigm or cognitive theory. In addition, current systems suffer from three other drawbacks:
1. They are not designed to integrate directly into end user applications.
2. They are not architected to take advantage of multiple methods.
3. They are not designed to deal with aggregate objects.